中国激光, 2020, 47 (2): 0207024, 网络出版: 2020-02-21   

基于噪声校正主成分分析的压缩感知STORM超分辨图像重构 下载: 1329次

Compressed Sensing STORM Super-Resolution Image Reconstruction Based on Noise Correction-Principal Component Analysis Preprocessing Algorithm
作者单位
深圳大学物理与光电工程学院, 生物医学光子学研究中心, 光电子器件与系统广东省/教育部重点实验室, 广东 深圳 518060
引用该论文

潘文慧, 陈秉灵, 张建国, 顾振宇, 熊佳, 张丹, 杨志刚, 屈军乐. 基于噪声校正主成分分析的压缩感知STORM超分辨图像重构[J]. 中国激光, 2020, 47(2): 0207024.

Pan Wenhui, Chen Bingling, Zhang Jianguo, Gu Zhenyu, Xiong Jia, Zhang Dan, Yang Zhigang, Qu Junle. Compressed Sensing STORM Super-Resolution Image Reconstruction Based on Noise Correction-Principal Component Analysis Preprocessing Algorithm[J]. Chinese Journal of Lasers, 2020, 47(2): 0207024.

参考文献

[1] Yu J. Single-molecule studies in live cells[J]. Annual Review of Physical Chemistry, 2016, 67(1): 565-585.

[2] Xia T, Li N, Fang X H. Single-molecule fluorescence imaging in living cells[J]. Annual Review of Physical Chemistry, 2013, 64(1): 459-480.

[3] Thompson M A, Lew M D, Moerner W E. Extending microscopic resolution with single-molecule imaging and active control[J]. Annual Review of Biophysics, 2012, 41(1): 321-342.

[4] Joo C, Balci H, Ishitsuka Y, et al. Advances in single-molecule fluorescence methods for molecular biology[J]. Annual Review of Biochemistry, 2008, 77(1): 51-76.

[5] 周兴, 但旦, 千佳, 等. 结构光照明显微中的超分辨图像重建研究[J]. 光学学报, 2017, 37(3): 0318001.

    Zhou X, Dan D, Qian J, et al. Super-resolution reconstruction theory in structured illumination microscopy[J]. Acta Optica Sinica, 2017, 37(3): 0318001.

[6] Rust M J, Bates M, Zhuang X W. Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM)[J]. Nature Methods, 2006, 3(10): 793-796.

[7] Huang B, Wang W, Bates M, et al. Three-dimensional super-resolution imaging by stochastic optical reconstruction microscopy[J]. Science, 2008, 319(5864): 810-813.

[8] 潘文慧, 李文, 屈璟涵, 等. 单分子定位超分辨显微成像有机荧光探针的研究进展[J]. 应用化学, 2019, 36(3): 269-281.

    Pan W H, Li W, Qu J H, et al. Research progress on organic fluorescent probes for single molecule localization microscopy[J]. Chinese Journal of Applied Chemistry, 2019, 36(3): 269-281.

[9] Gordon M P, Ha T, Selvin P R. Single-molecule high-resolution imaging with photobleaching[J]. Proceedings of the National Academy of Sciences of the United States of America, 2004, 101(17): 6462-6465.

[10] Jones S A, Shim S H, He J, et al. Fast, three-dimensional super-resolution imaging of live cells[J]. Nature Methods, 2011, 8(6): 499-505.

[11] Lee A, Tsekouras K, Calderon C, et al. Unraveling the thousand word picture: an introduction to super-resolution data analysis[J]. Chemical Reviews, 2017, 117(11): 7276-7330.

[12] Sage D, Kirshner H, Pengo T, et al. Quantitative evaluation of software packages for single-molecule localization microscopy[J]. Nature Methods, 2015, 12(8): 717-724.

[13] Small A, Stahlheber S. Fluorophore localization algorithms for super-resolution microscopy[J]. Nature Methods, 2014, 11(3): 267-279.

[14] Robbins M S, Hadwen B J. The noise performance of electron multiplying charge-coupled devices[J]. IEEE Transactions on Electron Devices, 2003, 50(5): 1227-1232.

[15] Zhu L, Zhang W, Elnatan D, et al. Faster STORM using compressed sensing[J]. Nature Methods, 2012, 9(7): 721-723.

[16] Ilovitsh T, Meiri A, Ebeling C G, et al. Improved localization accuracy in stochastic super-resolution fluorescence microscopy by K-factor image deshadowing[J]. Biomedical Optics Express, 2014, 5(1): 244-258.

[17] Jolliffe IT. Principal component analysis[M]. 2nd ed. New York: Springer-Verlag, 2002.

[18] Le Marois A, Labouesse S, Suhling K, et al. Noise-Corrected Principal Component Analysis of fluorescence lifetime imaging data[J]. Journal of Biophotonics, 2017, 10(9): 1124-1133.

[19] Liu X, Zhang B, Luo J W, et al. 4-D reconstruction for dynamic fluorescence diffuse optical tomography[J]. IEEE Transactions on Medical Imaging, 2012, 31(11): 2120-2132.

[20] Prats-Montalbán J M, de Juan A, Ferrer A. Multivariate image analysis: a review with applications[J]. Chemometrics and Intelligent Laboratory Systems, 2011, 107(1): 1-23.

[21] Pedersen F, Bergströme M, Bengtsson E, et al. Principal component analysis of dynamic positron emission tomography images[J]. European Journal of Nuclear Medicine, 1994, 21(12): 1285-1292.

[22] 刘俊秀, 杜彬, 邓玉强, 等. 基于差分-主成分分析-支持向量机的有机化合物太赫兹吸收光谱识别方法[J]. 中国激光, 2019, 46(6): 0614039.

    Liu J X, Du B, Deng Y Q, et al. Terahertz-spectral identification of organic compounds based on differential PCA-SVM method[J]. Chinese Journal of Lasers, 2019, 46(6): 0614039.

[23] Quan T W, Zeng S Q, Huang Z L. Localization capability and limitation of electron-multiplying charge-coupled, scientific complementary metal-oxide semiconductor, and charge-coupled devices for superresolution imaging[J]. Journal of Biomedical Optics, 2010, 15(6): 066005.

[24] Thompson R E, Larson D R, Webb W W. Precise nanometer localization analysis for individual fluorescent probes[J]. Biophysical Journal, 2002, 82(5): 2775-2783.

[25] O'Connor DV, Phillips D. Time-correlated single photon counting[M]. USA: Academic Press, 1984: 1- 35.

[26] Wang Z, Bovik A C, Sheikh H R, et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 2004, 13(4): 600-612.

[27] Collection of referencedatasets[DB/OL]. ( 2018-11-30)[2019-10-17]. http:∥bigwww.epfl.ch/smlm/datasets/.

[28] Nieuwenhuizen R P J, Lidke K A, Bates M, et al. Measuring image resolution in optical nanoscopy[J]. Nature Methods, 2013, 10(6): 557-562.

潘文慧, 陈秉灵, 张建国, 顾振宇, 熊佳, 张丹, 杨志刚, 屈军乐. 基于噪声校正主成分分析的压缩感知STORM超分辨图像重构[J]. 中国激光, 2020, 47(2): 0207024. Pan Wenhui, Chen Bingling, Zhang Jianguo, Gu Zhenyu, Xiong Jia, Zhang Dan, Yang Zhigang, Qu Junle. Compressed Sensing STORM Super-Resolution Image Reconstruction Based on Noise Correction-Principal Component Analysis Preprocessing Algorithm[J]. Chinese Journal of Lasers, 2020, 47(2): 0207024.

本文已被 8 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!